Course Overview:
This course offered at IRES is designed to introduce participants to the applications of machine learning techniques in data analysis using Stata. As the role of machine learning in decision-making processes continues to expand, this course equips participants with the necessary tools to apply machine learning algorithms to solve complex problems in economics, finance, and social sciences. The course covers essential machine learning methods such as classification, regression, clustering, and predictive modeling. Through practical exercises and real-world datasets, participants will learn how to use Stata’s capabilities to apply machine learning techniques for data-driven insights and decision-making.
Course Duration:
10 Days
Target Audience:
- Data scientists
- Economists
- Financial analysts
- Statisticians
- Researchers in social and behavioral sciences
- Professionals interested in applying machine learning to data analysis
Personal Impact:
- Learn machine learning techniques and how to apply them using Stata.
- Develop the ability to conduct data-driven predictions and analysis using machine learning.
- Enhance your analytical skillset for handling large and complex datasets.
Organizational Impact:
- Improve decision-making processes with advanced data-driven insights.
- Foster innovation through the use of machine learning models in business strategy and research.
- Strengthen the organization’s competitive advantage by leveraging predictive analytics.
Course Level:
Course Objectives:
By the end of this course, participants will be able to:
- Understand the core principles of machine learning and its application in Stata.
- Implement machine learning algorithms, including regression, classification, and clustering techniques, using Stata.
- Utilize predictive modeling to make data-driven decisions.
- Apply machine learning to real-world datasets in economics, finance, and social sciences.
- Assess model performance and optimize machine learning models.
Course Outline
Module 1: Introduction to Machine Learning with Stata
- Overview of machine learning concepts and applications.
- Introduction to Stata as a tool for machine learning.
- Key differences between traditional statistical methods and machine learning.
- Setting up the Stata environment for machine learning tasks.
- Case Study: Identifying business trends using supervised learning in Stata.
Module 2: Data Preparation and Feature Engineering
- Importing and cleaning datasets in Stata.
- Handling missing data and outliers.
- Creating and transforming variables for analysis.
- Feature selection techniques for optimal model performance.
- Case Study: Preparing customer data for predictive modeling.
Module 3: Supervised Learning – Regression Techniques
- Implementing linear regression models in Stata.
- Logistic regression for binary outcomes.
- Evaluating model performance using appropriate metrics.
- Regularization techniques to improve model accuracy.
- Case Study: Predicting employee turnover using regression models.
Module 4: Supervised Learning – Classification Techniques
- Introduction to classification algorithms.
- Decision trees and random forests in Stata.
- Evaluating classifier performance with confusion matrices.
- Hyperparameter tuning for better classification results.
- Case Study: Credit risk classification for financial institutions.
Module 5: Unsupervised Learning – Clustering Techniques
- Understanding clustering and its applications.
- Implementing k-means clustering in Stata.
- Hierarchical clustering for data segmentation.
- Interpreting clustering results for actionable insights.
- Case Study: Market segmentation for targeted marketing strategies.
Module 6: Dimensionality Reduction Techniques
- The importance of dimensionality reduction in machine learning.
- Principal Component Analysis (PCA) in Stata.
- Applying factor analysis to reduce feature space.
- Visualizing high-dimensional data effectively.
- Case Study: Reducing dimensions in genomic datasets for disease prediction.
Module 7: Model Evaluation and Validation
- Splitting data into training and testing sets in Stata.
- Cross-validation techniques for robust model assessment.
- Understanding overfitting and underfitting in machine learning models.
- Comparing models to select the best-performing algorithm.
- Case Study: Evaluating sales forecasting models for a retail chain.
Module 8: Advanced Machine Learning Techniques
- Introduction to ensemble methods: boosting and bagging.
- Implementing Stata plugins for advanced machine learning algorithms.
- Exploring support vector machines (SVM) in Stata.
- Practical applications of advanced algorithms in real-world problems.
- Case Study: Fraud detection in financial transactions using ensemble techniques.
Module 9: Automating Machine Learning with Stata
- Writing scripts for repetitive machine learning tasks.
- Automating model training and evaluation.
- Leveraging Stata macros and loops for efficiency.
- Building reproducible workflows for machine learning projects.
- Case Study: Automating customer churn prediction for telecom companies.
Module 10: Applications of Machine Learning in Stata
- Applying machine learning to health, economics, and social sciences.
- Ethical considerations in machine learning applications.
- Integrating Stata with other tools for advanced machine learning workflows.
- Emerging trends and future of machine learning in Stata.
- Case Study: Real-world implementation of predictive models in policy-making.
Related Courses
Course Administration Details:
METHODOLOGY
The instructor-led trainings are delivered using a blended learning approach and comprise presentations, guided sessions of practical exercise, web-based tutorials, and group work. Our facilitators are seasoned industry experts with years of experience, working as professionals and trainers in these fields. All facilitation and course materials will be offered in English. The participants should be reasonably proficient in English.
ACCREDITATION
Upon successful completion of this training, participants will be issued an Indepth Research Institute (IRES) certificate certified by the National Industrial Training Authority (NITA).
TRAINING VENUE
The training will be held at IRES Training Centre. The course fee covers the course tuition, training materials, two break refreshments, and lunch. All participants will additionally cater to their travel expenses, visa application, insurance, and other personal expenses.
ACCOMMODATION AND AIRPORT PICKUP
Accommodation and airport pickup are arranged upon request. For reservations contact the Training Officer.
- Email: [email protected]
- Phone: +254715 077 817
TAILOR-MADE
This training can also be customized to suit the needs of your institution upon request. You can have it delivered in our IRES Training Centre or at a convenient location. For further inquiries, please contact us on:
- Email: [email protected]
- Phone: +254715 077 817
PAYMENT
Payment should be transferred to the IRES account through a bank on or before the start of the course. Send proof of payment to [email protected]
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